A simple model predicts UGT-mediated metabolism

Dang, Na Le; Hughes, Tyler B.; Krishnamurthy, Varun K.; Swamidass, S. Joshua

doi:10.1093/bioinformatics/btw350

Cited by 57 publications

(43 citation statements)

References 22 publications

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“…In contrast to its predecessor, outputs are represented as probabilities rather than rank orderings. Xenosite is available as a web server (Matlock, Hughes, & Swamidass, 2015) and was extended to non-CYP applications such as UGT-mediated metabolism prediction (Dang, Hughes, Krishnamurthy, & Swamidass, 2016).…”

Section: Machine Learningmentioning

confidence: 99%

Computational methods and tools to predict cytochrome P450 metabolism for drug discovery

2019

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In this review, we present important, recent developments in the computational prediction of cytochrome P450 (CYP) metabolism in the context of drug discovery. We discuss in silico models for the various aspects of CYP metabolism prediction, including CYP substrate and inhibitor predictors, site of metabolism predictors (i.e., metabolically labile sites within potential substrates) and metabolite structure predictors. We summarize the different approaches taken by these models, such as rule‐based methods, machine learning, data mining, quantum chemical methods, molecular interaction fields, and docking. We highlight the scope and limitations of each method and discuss future implications for the field of metabolism prediction in drug discovery.

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Section: Machine Learningmentioning

confidence: 99%

Computational methods and tools to predict cytochrome P450 metabolism for drug discovery

2019

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“…The contemporary prediction methods have a good accuracy for reactions catalyzed by certain enzymes or enzyme classes, like the cytochrome P450 (CYP) superfamily (for examples, see references ). Recently, advances in non‐CYP SoM prediction have been made, and models for selected phase II reactions (glutathionation, glucuronidation) have been published . However, we are aware of only two holistic SoM models that can predict both phase I and phase II metabolism and are able to generalize over different enzyme families .…”

Section: Introductionmentioning

confidence: 99%

“…R ecently,a dvances in non-CYP SoM prediction have been made, [10] and modelsfor selected phase II reactions (glutathionation, glucuronidation) have been published. [11,12] However,w ea re aware of only two holistic SoM models that can predict both phase Ia nd phase II metabolism and are able to generalize over differente nzyme families. [13,14] Prediction tools that have ab road scope often rely on ligandbased machine learning strategies.…”

Section: Introductionmentioning

confidence: 99%

MetScore: Site of Metabolism Prediction Beyond Cytochrome P450 Enzymes

Finkelmann

Goldmann²,

Schneider

et al. 2018

ChemMedChem

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The metabolism of xenobiotics by humans and other organisms is a complex process involving numerous enzymes that catalyze phase I (functionalization) and phase II (conjugation) reactions. Herein we introduce MetScore, a machine learning model that can predict both phase I and phase II reaction sites of drugs in a single prediction run. We developed cheminformatics workflows to filter and process reactions to obtain suitable phase I and phase II data sets for model training. Employing a recently developed molecular representation based on quantum chemical partial charges, we constructed random forest machine learning models for phase I and phase II reactions. After combining these models with our previous cytochrome P450 model and calibrating the combination against Bayer in-house data, we obtained the MetScore model that shows good performance, with Matthews correlation coefficients of 0.61 and 0.76 for diverse phase I and phase II reaction types, respectively. We validated its potential applicability to lead optimization campaigns for a new and independent data set compiled from recent publications. The results of this study demonstrate the usefulness of quantum-chemistry-derived molecular representations for reactivity prediction.

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“…This work was based off an earlier model, Xenosite, an ANN‐based model for P450 metabolism on small‐molecules, which despite being a shallow network, was already outperforming the accuracy of SVM‐based models by as much as 5% . Further improvements were subsequently achieved by investigating the effect of using different types of molecular fingerprints for modeling P450 metabolism, where they discovered that further accuracy gains can be achieved using a consensus model utilizing different fingerprint types, and a related sister model that predicted the site of glucoronidation metabolism . In their more recent work on predicting epoxide‐based toxicity, Swamidass and coworkers designed a 4‐layer DNN architecture, and trained the model on a database of 702 epoxidation reactions, and identified SOEs with 94.9% AUC performance, and separated (i.e., classified) epoxidized and non‐epoxidized molecules with 79.3% AUC .…”

Section: Computer‐aided Drug Designmentioning

confidence: 99%

Deep learning for computational chemistry

2017

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The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on multilayer neural networks. Within the last few years, we have seen the transformative impact of deep learning in many domains, particularly in speech recognition and computer vision, to the extent that the majority of expert practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties that distinguish them from traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including QSAR, virtual screening, protein structure prediction, quantum chemistry, materials design and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against nonneural networks state-of-the-art models across disparate research topics, and deep neural network based models often exceeded the "glass ceiling" expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a valuable tool for computational chemistry. 3 IntroductionDeep Learning is the key algorithm used in the development of AlphaGo, a Deep learning is a machine learning algorithm, not unlike those already in use in various applications in computational chemistry, from computer-aided drug design to materials property prediction. 5-8 Amongst some of its more high profile achievements include the Merck activity prediction challenge in 2012, where a deep neural network not only won the competition and outperformed Merck's internal baseline model, but did so without having a single chemist or biologist in their team. In a repeated success by a different research team, deep learning models achieved top positions in the Tox21 toxicity prediction challenge issued by NIH in 2014. 9 The unusually stellar performance of deep learning models in both predicting activity and toxicity in these recent examples, originate from the unique characteristics that distinguishes deep learning from traditional machine learning algorithms.For those unfamiliar with the intricacies of machine learning algorithms, we will highlight some of the key differences between traditional (shallow) machine learning and deep learning.The simplest example of a machine learning algorithm would be the ubiquitous least-squares linear regression. In linear regression, the underlying nature of the model is known (linear in th...

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A simple model predicts UGT-mediated metabolism

Abstract: The UGT metabolism predictor developed in this study is available at http://swami.wustl.edu/xenosite/p/ugt CONTACT: : swamidass@wustl.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Cited by 57 publications

References 22 publications

Computational methods and tools to predict cytochrome P450 metabolism for drug discovery

Computational methods and tools to predict cytochrome P450 metabolism for drug discovery

MetScore: Site of Metabolism Prediction Beyond Cytochrome P450 Enzymes

Deep learning for computational chemistry

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